Bilgisayar Bilimleri

Yazarlar: Gaffari ÇELİK, Muhammed Fatih TALU

Konular:Bilgisayar Bilimleri, Bilgi Sistemleri

Anahtar Kelimeler:Pattern spread,Pixel-based,Patch-based,Pyramid-based

Özet: The performance of deep learning approaches is directly proportional to the size of the data set used in the training phase. For this reason, large data sets are currently being built to solve problems such as image classification, segmentation or object capture. For example Alexnet database 1.2M image and 1K categorie; Imagenet, 15M image and 22K categorie; Yahoo Flickr has 100M image and 2K categorization. However, manual assignment of imagery to categories is undoubtedly the greatest disadvantage of deep learning approaches. Categorizing images (labeling) is a very troublesome and error-prone process. In order to remove the possibility of this difficulty and error, it is suggested to use data sets containing synthetic images instead of real images. Synthetic image production includes phases of pattern and pattern production. It is possible to synthetically produce an object by constructing shape and pattern descriptive models. This study covers pattern descriptive methods (Patch, Pixel, Pyramid). These methods are aimed at generating a high-dimensional image by spreading the pattern out of a small pattern information obtained from a real image. As a result of comparison with accuracy, time and noise sensitivity criteria, the pach-based method is considered to be the most suitable pattern spreading method.

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